Organization and Energy Transfer of Fused Aromatic Hydrocarbon Guests within Anion-Confining Nanochannel MOFs.

The self-assembly of Zn(II) ions with 1,3,5-tris(isonicotinoyloxyethyl)cyanurate produces new topological (4(2)⋅12(4))3(4(3))4 2D metal-organic frameworks (MOFs) with anion-confining cages. The eclipsed assembly of each 2D MOF by π-π stacking of cyanurate moieties (3.352(5) Å) forms 3D MOFs consisting of nanochannels (10.5 Å). Two of the three anions are confined in each peanut-type cage, resulting in hydrophobicity of the nanochannels. The hydrophobic nanochannel effectively adsorbs a wide range of fused aromatic hydrocarbons (FAHs) as monomers or dimers, rendering it potentially highly useful as an energy-transfer material.

SeqAFNet: A Beat-Wise Sequential Neural Network for Atrial Fibrillation Classification in Adhesive Patch-Type Electrocardiographs.

Due to their convenience, adhesive patch-type electrocardiographs are commonly used for arrhythmia screening. This study aimed to develop a reliable method that can improve the classification performance of atrial fibrillation (AF) using these devices based on the 2020 European Society of Cardiology (ESC) guidelines for AF diagnosis in clinical practice. We developed a deep learning model that utilizes RR interval frames for precise, beat-wise classification of electrocardiogram (ECG) signals. This model is specifically designed to sequentially classify each R peak on the ECG, considering the rhythms surrounding each beat. It features a two-stage bidirectional Recurrent Neural Network (RNN) with a many-to-many architecture, which is particularly optimized for processing sequential and time-series data. The structure aims to extract local features and capture long-term dependencies associated with AF. After inference, outputs which indicating either AF or non-AF, derived from various temporal sequences are combined through an ensembling technique to enhance prediction accuracy. We collected AF data from a clinical trial that utilized the MEMO Patch, an adhesive patch-type electrocardiograph. When trained on public databases, the model demonstrated high accuracy on the patch dataset (accuracy: 0.986, precision: 0.981, sensitivity: 0.979, specificity: 0.992, and F1 score: 0.98), maintaining consistent performance across public datasets. SeqAFNet was robust for AF classification, making it a potential tool in real-world applications.

CNN-based Two Step R Peak Detection Method: Combining Segmentation and Regression.

For semantic segmentation, U-Net provides an end-to-end trainable framework to detect multiple class objects from background. Due to its great achievements in computer vision tasks, U-Net has broadened its application to biomedical signal processing, especially, segmentation of waveforms in ECG signal. Despite its superior performance for QRS complex detection to other traditional signal processing methods, direct application of the U-Net to R peak detection has limitation since the U-Net structures tend to predict high probability around true peak. Such multiple detection results require additional process to determine a unique peak location in each QRS complex. In this study, we use a regression process to detect R peak instead of pixel-wise classification. Such regression process guarantees a unique peak location prediction. We collect data from resting ECG systems and wearable ECG devices as well as public ECG databases and the proposed model is trained on various combinations of the data sources. Especially, we investigate the robustness of the model for input data from the wearable devices when the model is trained by data from heterogeneous devices.

Three-dimensional measurement and registration accuracy of a third-generation optical tracking system for navigational maxillary orthognathic surgery.

OBJECTIVE: The purpose of this study was to evaluate the accuracy of an optical tracking system during reference point localization, measurement, and registration of skull models for navigational maxillary orthognathic surgery. STUDY DESIGN: Accuracy was first evaluated on the basis of the position recording discrepancy at a static point and at 2 points of fixed lengths. Ten reference points were measured on a skull model at 7 different locations, and their measurements were compared with predicted positions by using 4 registration methods. Finally, positional tracking of reference points for simulated maxillary surgery was performed and compared with laser scan data. RESULTS: The average linear measurement discrepancy was 0.28 mm, and the mean measurement discrepancy with the 5 registered cranial points was 1.53 mm. The average measurement discrepancy after maxillary surgery was 1.91 mm (for impaction) and 1.56 mm (for advancement). The registration discrepancy in jitter and point registration on the y-axis was significantly greater than on the other axes. CONCLUSIONS: The optical tracking system seems clinically acceptable for precise tracking of the maxillary position during navigational orthognathic surgery, notwithstanding the chance of greater measurement error on the y-axis.

Automatic Estimation of Fetal Abdominal Circumference From Ultrasound Images.

Ultrasound diagnosis is routinely used in obstetrics and gynecology for fetal biometry, and owing to its time-consuming process, there has been a great demand for automatic estimation. However, the automated analysis of ultrasound images is complicated because they are patient specific, operator dependent, and machine specific. Among various types of fetal biometry, the accurate estimation of abdominal circumference (AC) is especially difficult to perform automatically because the abdomen has low contrast against surroundings, nonuniform contrast, and irregular shape compared to other parameters. We propose a method for the automatic estimation of the fetal AC from two-dimensional ultrasound data through a specially designed convolutional neural network (CNN), which takes account of doctors' decision process, anatomical structure, and the characteristics of the ultrasound image. The proposed method uses CNN to classify ultrasound images (stomach bubble, amniotic fluid, and umbilical vein) and Hough transformation for measuring AC. We test the proposed method using clinical ultrasound data acquired from 56 pregnant women. Experimental results show that, with relatively small training samples, the proposed CNN provides sufficient classification results for AC estimation through the Hough transformation. The proposed method automatically estimates AC from ultrasound images. The method is quantitatively evaluated and shows stable performance in most cases and even for ultrasound images deteriorated by shadowing artifacts. As a result of experiments for our acceptance check, the accuracies are 0.809 and 0.771 with expert 1 and expert 2, respectively, whereas the accuracy between the two experts is 0.905. However, for cases of oversized fetus, when the amniotic fluid is not observed or the abdominal area is distorted, it could not correctly estimate AC.

Machine-learning-based automatic identification of fetal abdominal circumference from ultrasound images.

OBJECTIVE: Obstetricians mainly use ultrasound imaging for fetal biometric measurements. However, such measurements are cumbersome. Hence, there is urgent need for automatic biometric estimation. Automated analysis of ultrasound images is complicated owing to the patient-specific, operator-dependent, and machine-specific characteristics of such images. APPROACH: This paper proposes a method for the automatic fetal biometry estimation from 2D ultrasound data through several processes consisting of a specially designed convolutional neural network (CNN) and U-Net for each process. These machine learning techniques take clinicians' decisions, anatomical structures, and the characteristics of ultrasound images into account. The proposed method is divided into three steps: initial abdominal circumference (AC) estimation, AC measurement, and plane acceptance checking. MAIN RESULTS: A CNN is used to classify ultrasound images (stomach bubble, amniotic fluid, and umbilical vein), and a Hough transform is used to obtain an initial estimate of the AC. These data are applied to other CNNs to estimate the spine position and bone regions. Then, the obtained information is used to determine the final AC. After determining the AC, a U-Net and a classification CNN are used to check whether the image is suitable for AC measurement. Finally, the efficacy of the proposed method is validated by clinical data. SIGNIFICANCE: Our method achieved a Dice similarity metric of [Formula: see text] for AC measurement and an accuracy of 87.10% for our acceptance check of the fetal abdominal standard plane.

Inverse Problem for Color Doppler Ultrasound-Assisted Intracardiac Blood Flow Imaging.

For the assessment of the left ventricle (LV), echocardiography has been widely used to visualize and quantify geometrical variations of LV. However, echocardiographic image itself is not sufficient to describe a swirling pattern which is a characteristic blood flow pattern inside LV without any treatment on the image. We propose a mathematical framework based on an inverse problem for three-dimensional (3D) LV blood flow reconstruction. The reconstruction model combines the incompressible Navier-Stokes equations with one-direction velocity component of the synthetic flow data (or color Doppler data) from the forward simulation (or measurement). Moreover, time-varying LV boundaries are extracted from the intensity data to determine boundary conditions of the reconstruction model. Forward simulations of intracardiac blood flow are performed using a fluid-structure interaction model in order to obtain synthetic flow data. The proposed model significantly reduces the local and global errors of the reconstructed flow fields. We demonstrate the feasibility and potential usefulness of the proposed reconstruction model in predicting dynamic swirling patterns inside the LV over a cardiac cycle.

A Reconstruction Method of Blood Flow Velocity in Left Ventricle Using Color Flow Ultrasound.

Vortex flow imaging is a relatively new medical imaging method for the dynamic visualization of intracardiac blood flow, a potentially useful index of cardiac dysfunction. A reconstruction method is proposed here to quantify the distribution of blood flow velocity fields inside the left ventricle from color flow images compiled from ultrasound measurements. In this paper, a 2D incompressible Navier-Stokes equation with a mass source term is proposed to utilize the measurable color flow ultrasound data in a plane along with the moving boundary condition. The proposed model reflects out-of-plane blood flows on the imaging plane through the mass source term. The boundary conditions to solve the system of equations are derived from the dimensions of the ventricle extracted from 2D echocardiography data. The performance of the proposed method is evaluated numerically using synthetic flow data acquired from simulating left ventricle flows. The numerical simulations show the feasibility and potential usefulness of the proposed method of reconstructing the intracardiac flow fields. Of particular note is the finding that the mass source term in the proposed model improves the reconstruction performance.

Truncated trigonal prismatic tubular crystals consisting of a zeolite L-mimic metal-organic framework.

Research on the construction, crystal morphology, and functions of a novel zeolite L-mimic metal-organic framework (ZLMOF) was carried out. Treatment of the tubular crystals with AgBF4 in acetone at 40 °C smoothly coated the surface of the ZLMOF crystals with silver(0) nanoparticles.